Instructions to use joaogante/test_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joaogante/test_text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="joaogante/test_text")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("joaogante/test_text") model = AutoModelForMaskedLM.from_pretrained("joaogante/test_text") - Notebooks
- Google Colab
- Kaggle
Add TF weights
#4
by joaogante - opened
Model converted by the (transformers' pt_to_tf CLI)[https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py]
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=4.578e-05; Maximum converted output difference=4.578e-05.